The Tri-lnstitutional Training Program in Computational Biology and Medicine (CBM) takes advantage of the outstanding educational and research resources of Cornell University in Ithaca, NY, the Weill Cornell Medical College in New York City, and the Sloan-Kettering Institute (the research arm of the Memorial Sloan- Kettering Cancer Center) to train computational biologists in the interdisciplinary approaches they need to solve the complex problems that characterize biology and medicine. The CBM training environment is designed to address the unique challenges of training scientists in this area and is characterized by: (i) coursework in both quantitative and biological sciences; (ii) research rotations to enable a well-informed thesis topic selection; (iii) journal clubs and research-in-progress series to enhance program cohesion and ensure fluency in relevant disciplines; (iv) thesis research in one of a diverse array of basic to translational computational and experimental laboratories; (v) both quantitative and biological mentorship in order to ensure training balance and breadth; (vi) encouragement to engage in hybrid computational/experimental projects to foster connections between theory and experiment. The enthusiastic support for the CBM program by the Tri-lnstitutional consortium has enabled the program to enroll and support 6 students annually. However, now that the program is well established and entering its sixth year, it is clear that it would benefit from an increase in its most vital component - outstanding students. To that end, the goal of this T32 proposal is to receive funding to increase our annual admission to 9 students. The justifications for such growth include: (i) an unmet demand by CBM faculty for students; (ii) the CBM applicant pool is deep enough to allow for an increase without either lowering quality or decreasing the percentage of admitted domestic students; and (iii) a national need to train more computational biologists. By enabling such growth, T32 funding would aid the CBM program in achieving its mission of training the next generation of scientist to use computational and analytical methods, often integrated with experimental and clinical studies, to solve complex interdisciplinary problems in biology.